SecureMA: protecting participant privacy in genetic association meta-analysis.
AUTHORS
- PMID: 25147357 [PubMed].
ABSTRACT
Motivation
Sharing genomic data is crucial to support scientific investigation such as genome-wide association studies (GWAS). However, recent investigations suggest the privacy of the individual participants in these studies can be compromised, leading to serious concerns and consequences, such as overly restricted access to data.
Results
We introduce a novel cryptographic strategy to securely perform meta-analysis for genetic association studies in large consortia. Our methodology is useful for supporting joint studies among disparate data sites, where privacy or confidentiality is of concern. We validate our method using three multi-site association studies. Our research shows that genetic associations can be analyzed efficiently and accurately across sub-study sites, without leaking information on individual participants and site-level association summaries.
Availability
Our software for secure meta-analysis of genetic association studies, SecureMA, is publicly-available at http://github.com/XieConnect/SecureMA Our customized secure computation framework is also publicly-available at http://github.com/XieConnect/CircuitService CONTACT: b.malin@vanderbilt.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
© The Author (2014). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Motivation
Sharing genomic data is crucial to support scientific investigation such as genome-wide association studies (GWAS). However, recent investigations suggest the privacy of the individual participants in these studies can be compromised, leading to serious concerns and consequences, such as overly restricted access to data.
Results
We introduce a novel cryptographic strategy to securely perform meta-analysis for genetic association studies in large consortia. Our methodology is useful for supporting joint studies among disparate data sites, where privacy or confidentiality is of concern. We validate our method using three multi-site association studies. Our research shows that genetic associations can be analyzed efficiently and accurately across sub-study sites, without leaking information on individual participants and site-level association summaries.
Availability
Our software for secure meta-analysis of genetic association studies, SecureMA, is publicly-available at http://github.com/XieConnect/SecureMA Our customized secure computation framework is also publicly-available at http://github.com/XieConnect/CircuitService CONTACT: b.malin@vanderbilt.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
© The Author (2014). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Tags: 2014